91 libraries, 16 updated this year
Code to read rather than install: Jupyter notebook collections, the companion repositories of well-known books, and full solutions to Kaggle competitions. Popular picks include donnemartin's data-science-ipython-notebooks, the notebooks behind Aurélien Géron's Hands-On Machine Learning, Wes McKinney's Python for Data Analysis, and Homemade Machine Learning, which implements classic algorithms from scratch.
91 libraries
Continually updated Data Science Python Notebooks: Spark, Hadoop MapReduce, HDFS, AWS, Kaggle, scikit-learn, matplotlib, pandas, NumPy, SciPy, and various command lines.
Python
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media
Python
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Python
Jupyter metapackage for installation and documentation
Python
"I learned Python by hacking first, and getting serious later. I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."
Python
Notebooks and code for the book "Introduction to Machine Learning with Python"
Python
A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks
Python
IPython notebooks from Data School's video tutorials on scikit-learn.
Python
An easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search.
Python
TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.
Python
Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.
Python
Text and supporting code for Think OS: A Brief Introduction to Operating Systems.
Python
Winning solution for the Galaxy Challenge on Kaggle.
Python
> source code and experiments results for Home Credit Default Risk.
Python
This repository contains Jupyter notebooks implementing the algorithms found in the book and summary of the textbook.
Python
Digital Signal Processing (filtering, periodograms, spectrograms, window functions).
Julia
Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others.
Python
Sentiment classifier using word sense disambiguation.
Python
> source code and experiments results for 2018 Data Science Bowl.
Python> source code for Toxic Comment Classification Challenge.
PythonKaggle Submission for "Detecting Insults in Social Commentary".
PythonKanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run Cabocha(ISO--8859-1 configured) in Python.
Python
Series of notebooks for learning scikit-learn.
Python
Concise cheat sheets containing machine learning best practices.
SAS> source code and experiments results for TGS Salt Identification Challenge.
PythonCode for Allen Downey's book Think Complexity.
Python
A minimal, educational, Pythonic implementation of autograd (~100 loc).
Python
IPython notebooks for a complete course around understanding Nervana's Neon.
Python
Code for the Kaggle acquire valued shoppers challenge.
Python> source code and experiments results for Airbus Ship Detection Challenge.
Python
Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
Python
Predicting job salaries from ads - a Kaggle competition.
Python
